Traffic signal timing estimation using an artificial neural network model

ABSTRACT

A method and apparatus for traffic signal timing estimation are disclosed. Traffic signal timing estimation may include a vehicle identifying transportation network information representing a vehicle transportation network including an intersection. The transportation network information may include expected traffic control device state information corresponding to the intersection, such as an artificial neural network based machine learning model trained for the intersection. The vehicle may identify a route through the vehicle transportation network that includes the intersection based on the expected traffic control device state information and may control the traversal of the vehicle transportation network by the vehicle based on the expected traffic control device state information to minimize one or more operational cost metrics. Training the model may include identifying training data from input data previously generated and stored by a traffic control device controller of the intersection.

TECHNICAL FIELD

This disclosure relates to vehicle routing and navigation.

BACKGROUND

A vehicle, such as an autonomous vehicle, may traverse a route of travelfrom an origin to a destination. The vehicle may include a controlsystem that may generate and maintain the route of travel and maycontrol the vehicle to traverse the route of travel. Accordingly, amethod and apparatus for traffic signal timing estimation using anartificial neural network model may be advantageous.

SUMMARY

Disclosed herein are aspects, features, elements, implementations, andembodiments of traffic signal timing estimation using an artificialneural network model.

An aspect of the disclosed embodiments is a vehicle for traffic signaltiming estimation using an artificial neural network model. The vehiclemay include a processor configured to execute instructions stored on anon-transitory computer readable medium to identify transportationnetwork information representing a vehicle transportation network, thevehicle transportation network including a primary destination, whereinidentifying the transportation network information includes identifyingthe transportation network information such that the transportationnetwork information includes expected traffic control device stateinformation, wherein the expected traffic control device stateinformation is determined using an expected traffic control device statedetermination unit, wherein the expected traffic control device statedetermination unit implements a machine learning algorithm, wherein themachine learning algorithm includes an artificial neural networkalgorithm. The processor may be configured to execute instructionsstored on the non-transitory computer readable medium to identify aroute from an origin to the primary destination in the vehicletransportation network using the transportation network information,wherein the route includes a vehicle transportation networkintersection, and wherein the expected traffic control device stateinformation includes expected traffic control device state informationcorresponding to the vehicle transportation network intersection, andwherein using the transportation network information includes using theexpected traffic control device state information corresponding to thevehicle transportation network intersection. The vehicle may include atrajectory controller configured to operate the vehicle to traverse thevehicle transportation network from the origin to the primarydestination using the route.

Another aspect of the disclosed embodiments is a vehicle for trafficsignal timing estimation using an artificial neural network model. Thevehicle may include a processor configured to execute instructionsstored on a non-transitory computer readable medium to identifytransportation network information representing a vehicle transportationnetwork, the vehicle transportation network including a primarydestination, wherein identifying the transportation network informationincludes identifying the transportation network information such thatthe transportation network information includes expected traffic controldevice state information, wherein the expected traffic control devicestate information is determined using an expected traffic control devicestate determination unit, wherein the expected traffic control devicestate determination unit implements a machine learning algorithm,wherein the machine learning algorithm includes an artificial neuralnetwork algorithm. The processor may be configured to executeinstructions stored on the non-transitory computer readable medium toidentify a route from an origin to the primary destination in thevehicle transportation network using the transportation networkinformation, wherein the route includes a vehicle transportation networkintersection, and wherein the expected traffic control device stateinformation includes expected traffic control device state informationcorresponding to the vehicle transportation network intersection, andwherein using the transportation network information includes using theexpected traffic control device state information corresponding to thevehicle transportation network intersection. The vehicle may include atrajectory controller configured to operate the autonomous vehicle totraverse the vehicle transportation network from the origin to theprimary destination using the route using the expected traffic controldevice state information corresponding to the vehicle transportationnetwork intersection.

Another aspect of the disclosed embodiments is a vehicle for trafficsignal timing estimation using an artificial neural network model. Thevehicle may include a processor configured to execute instructionsstored on a non-transitory computer readable medium to identifytransportation network information representing a vehicle transportationnetwork, the vehicle transportation network including a primarydestination, wherein identifying the transportation network informationincludes identifying the transportation network information such thatthe transportation network information includes expected traffic controldevice state information, wherein the expected traffic control devicestate information is determined using an artificial neural networkalgorithm. The vehicle may identify the expected traffic control devicestate information such that the expected traffic control device stateinformation is determined by receiving traffic control information for avehicle actuated traffic control device controller associated with thevehicle transportation network intersection, evaluating the trafficcontrol information to identify a plurality of candidate traffic controlfeatures, determining a plurality of selected traffic control featuresfrom the plurality of candidate traffic control features, and trainingthe expected traffic control device state determination unitcorresponding to the vehicle transportation network intersection usingthe plurality of selected traffic control features. The processor may beconfigured to execute instructions stored on the non-transitory computerreadable medium to identify a route from an origin to the primarydestination in the vehicle transportation network using thetransportation network information, wherein the route includes a vehicletransportation network intersection, and wherein the expected trafficcontrol device state information includes expected traffic controldevice state information corresponding to the vehicle transportationnetwork intersection, and wherein using the transportation networkinformation includes using the expected traffic control device stateinformation corresponding to the vehicle transportation networkintersection, wherein the expected traffic control device stateinformation includes expected permitted right-of-way signal temporalinformation corresponding to the vehicle transportation networkintersection for the route. The vehicle may include a trajectorycontroller configured to operate the vehicle to traverse the vehicletransportation network from the origin to the primary destination usingthe route.

Variations in these and other aspects, features, elements,implementations, and embodiments of the methods, apparatus, procedures,and algorithms disclosed herein are described in further detailhereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

The various aspects of the methods and apparatuses disclosed herein willbecome more apparent by referring to the examples provided in thefollowing description and drawings in which:

FIG. 1 is a diagram of an example of a portion of a vehicle in which theaspects, features, and elements disclosed herein may be implemented;

FIG. 2 is a diagram of an example of a portion of a vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented;

FIG. 3 is a diagram of a portion of a vehicle transportation network inaccordance with this disclosure;

FIG. 4 is a diagram of a portion of a vehicle transportation networkincluding an intersection in accordance with this disclosure;

FIG. 5 is another diagram of a portion of a vehicle transportationnetwork including an intersection in accordance with this disclosure;

FIG. 6 is another diagram of a portion of a vehicle transportationnetwork including an intersection in accordance with this disclosure;

FIG. 7 is a diagram of an example of a method of traversing a vehicletransportation network using expected traffic control device stateinformation in accordance with this disclosure;

FIG. 8 is a diagram of an example of a method of identifying expectedtraffic control device state information in accordance with thisdisclosure;

FIG. 9 is a diagram of an example of a method of identifying a route inaccordance with this disclosure; and

FIG. 10 is a diagram of an example of a method of traversing a route inaccordance with this disclosure.

DETAILED DESCRIPTION

A vehicle, such as an autonomous vehicle, may travel from a point oforigin to a destination in a vehicle transportation network. Forexample, an autonomous vehicle may traverse the vehicle transportationnetwork without human intervention. The vehicle may include acontroller, which may perform vehicle routing and navigation. Thecontroller may generate a route of travel from the origin to thedestination based on vehicle information, environment information,vehicle transportation network information representing the vehicletransportation network, or a combination thereof. In an autonomousvehicle, the controller may output the route of travel to a trajectorycontroller that may operate the vehicle to travel from the origin to thedestination using the generated route.

In some embodiments, the vehicle transportation network information mayinclude expected traffic control device state information, such as amachine learning model, which may be an artificial neural network modelor a support vector regression model, which may be used by the vehicleto determine an expected signal state of a traffic control device for anintersection in the vehicle transportation network along a route oftraversal of the vehicle.

In some embodiments, the model may be trained for a respectiveintersection based on training data corresponding to the intersection.In some embodiments, the training data may be generated based onpreviously stored data indicating intersection conditions andintersection control device state information generated and storedduring one or more previous time periods. In some embodiments, trainingthe model may include evaluating the input data generated and stored bythe traffic control device to identify one or more relevant features,extracting the features and corresponding values from the input data,validating the extracted data, normalizing the validated data,temporally grouping the normalized data, and training the model pertemporal group using the normalized data groups.

In some embodiments, the vehicle may determine a route from an origin toa destination based on the trained model. For example, the vehicle mayidentifying an intersection in a candidate route, may determine anexpected time for the vehicle to reach the intersection using the route,may determine expected traffic conditions corresponding to the expectedtime for the vehicle to arrive at the intersection, may determine acorresponding expected signal state using the trained model, and mayselect or adjust the route based on the expected signal state. Inanother example, the vehicle may adjust a speed of the vehicle inresponse to an expected signal state. For example, the vehicle mayslightly increase or reduce speed to arrive at the intersection during apermitted right-of-way signal, so as to avoid stopping at a deniedright-of way signal for the intersection.

The embodiments of the methods disclosed herein, or any part or partsthereof, including and aspects, features, elements thereof, may beimplemented in a computer program, software, or firmware, or a portionthereof, incorporated in a tangible non-transitory computer-readable orcomputer-usable storage medium for execution by special purpose computeror processor.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more applicationprocessors, one or more Application Specific Integrated Circuits, one ormore Application Specific Standard Products; one or more FieldProgrammable Gate Arrays, any other type or combination of integratedcircuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, low power double data rate (LPDDR) memories, oneor more cache memories, one or more semiconductor memory devices, one ormore magnetic media, one or more optical media, one or moremagneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein. Insome embodiments, instructions, or a portion thereof, may be implementedas a special purpose processor, or circuitry, that may includespecialized hardware for carrying out any of the methods, algorithms,aspects, or combinations thereof, as described herein. In someimplementations, portions of the instructions may be distributed acrossmultiple processors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “example”, “embodiment”,“implementation”, “aspect”, “feature”, or “element” indicate serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “determine” and “identify”, or anyvariations thereof, includes selecting, ascertaining, computing, lookingup, receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices shown and described herein.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or”. That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

FIG. 1 is a diagram of an example of a vehicle, such as an autonomousvehicle, in which the aspects, features, and elements disclosed hereinmay be implemented. In some embodiments, a vehicle 1000 may include achassis 1100, a powertrain 1200, a controller 1300, wheels 1400, or anyother element or combination of elements of a vehicle. Although thevehicle 1000 is shown as including four wheels 1400 for simplicity, anyother propulsion device or devices, such as a propeller or tread, may beused. In FIG. 1, the lines interconnecting elements, such as thepowertrain 1200, the controller 1300, and the wheels 1400, indicate thatinformation, such as data or control signals, power, such as electricalpower or torque, or both information and power, may be communicatedbetween the respective elements. For example, the controller 1300 mayreceive power from the powertrain 1200 and may communicate with thepowertrain 1200, the wheels 1400, or both, to control the vehicle 1000,which may include accelerating, decelerating, steering, or otherwisecontrolling the vehicle 1000.

The powertrain 1200 may include a power source 1210, a transmission1220, a steering unit 1230, an actuator 1240, or any other element orcombination of elements of a powertrain, such as a suspension, a driveshaft, axles, or an exhaust system. Although shown separately, thewheels 1400 may be included in the powertrain 1200.

The power source 1210 may include an engine, a battery, or a combinationthereof. The power source 1210 may be any device or combination ofdevices operative to provide energy, such as electrical energy, thermalenergy, or kinetic energy. For example, the power source 1210 mayinclude an engine, such as an internal combustion engine, an electricmotor, or a combination of an internal combustion engine and an electricmotor, and may be operative to provide kinetic energy as a motive forceto one or more of the wheels 1400. In some embodiments, the power source1400 may include a potential energy unit, such as one or more dry cellbatteries, such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickelmetal hydride (NiMH), lithium-ion (Li-ion); solar cells; fuel cells; orany other device capable of providing energy.

The transmission 1220 may receive energy, such as kinetic energy, fromthe power source 1210, and may transmit the energy to the wheels 1400 toprovide a motive force. The transmission 1220 may be controlled by thecontrol unit 1300 the actuator 1240 or both. The steering unit 1230 maybe controlled by the control unit 1300 the actuator 1240 or both and maycontrol the wheels 1400 to steer the vehicle. The vehicle actuator 1240may receive signals from the controller 1300 and may actuate or controlthe power source 1210, the transmission 1220, the steering unit 1230, orany combination thereof to operate the vehicle 1000.

In some embodiments, the controller 1300 may include a location unit1310, an electronic communication unit 1320, a processor 1330, a memory1340, a user interface 1350, a sensor 1360, an electronic communicationinterface 1370, or any combination thereof. Although shown as a singleunit, any one or more elements of the controller 1300 may be integratedinto any number of separate physical units. For example, the userinterface 1350 and processor 1330 may be integrated in a first physicalunit and the memory 1340 may be integrated in a second physical unit.Although not shown in FIG. 1, the controller 1300 may include a powersource, such as a battery. Although shown as separate elements, thelocation unit 1310, the electronic communication unit 1320, theprocessor 1330, the memory 1340, the user interface 1350, the sensor1360, the electronic communication interface 1370, or any combinationthereof may be integrated in one or more electronic units, circuits, orchips.

In some embodiments, the processor 1330 may include any device orcombination of devices capable of manipulating or processing a signal orother information now-existing or hereafter developed, including opticalprocessors, quantum processors, molecular processors, or a combinationthereof. For example, the processor 1330 may include one or more generalpurpose processors, one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more integratedcircuits, one or more an Application Specific Integrated Circuits, oneor more Field Programmable Gate Array, one or more programmable logicarrays, one or more programmable logic controllers, one or more statemachines, or any combination thereof. The processor 1330 may beoperatively coupled with the location unit 1310, the memory 1340, theelectronic communication interface 1370, the electronic communicationunit 1320, the user interface 1350, the sensor 1360, the powertrain1200, or any combination thereof. For example, the processor may beoperatively coupled with the memory 1340 via a communication bus 1380.

The memory 1340 may include any tangible non-transitory computer-usableor computer-readable medium, capable of, for example, containing,storing, communicating, or transporting machine readable instructions,or any information associated therewith, for use by or in connectionwith the processor 1330. The memory 1340 may be, for example, one ormore solid state drives, one or more memory cards, one or more removablemedia, one or more read only memories, one or more random accessmemories, one or more disks, including a hard disk, a floppy disk, anoptical disk, a magnetic or optical card, or any type of non-transitorymedia suitable for storing electronic information, or any combinationthereof.

The communication interface 1370 may be a wireless antenna, as shown, awired communication port, an optical communication port, or any otherwired or wireless unit capable of interfacing with a wired or wirelesselectronic communication medium 1500. Although FIG. 1 shows thecommunication interface 1370 communicating via a single communicationlink, a communication interface may be configured to communicate viamultiple communication links. Although FIG. 1 shows a singlecommunication interface 1370, a vehicle may include any number ofcommunication interfaces.

The communication unit 1320 may be configured to transmit or receivesignals via a wired or wireless medium 1500, such as via thecommunication interface 1370. Although not explicitly shown in FIG. 1,the communication unit 1320 may be configured to transmit, receive, orboth via any wired or wireless communication medium, such as radiofrequency (RF), ultra violet (UV), visible light, fiber optic, wireline, or a combination thereof. Although FIG. 1 shows a singlecommunication unit 1320 and a single communication interface 1370, anynumber of communication units and any number of communication interfacesmay be used. In some embodiments, the communication unit 1320 mayinclude a dedicated short range communications (DSRC) unit, a wirelesssafety unit (WSU), or a combination thereof.

The location unit 1310 may determine geolocation information, such aslongitude, latitude, elevation, direction of travel, or speed, of thevehicle 1000. For example, the location unit may include a globalpositioning system (GPS) unit, such as a Wide Area Augmentation System(WAAS) enabled National Marine-Electronics Association (NMEA) unit, aradio triangulation unit, or a combination thereof. The location unit1310 can be used to obtain information that represents, for example, acurrent heading of the vehicle 1000, a current position of the vehicle1000 in two or three dimensions, a current angular orientation of thevehicle 1000, or a combination thereof.

The user interface 1350 may include any unit capable of interfacing witha person, such as a virtual or physical keypad, a touchpad, a display, atouch display, a heads-up display, a virtual display, an augmentedreality display, a speaker, a microphone, a haptic display, a featuretracking device, such as an eye-tracking device, a video camera, asensor, a printer, or any combination thereof. The user interface 1350may be operatively coupled with the processor 1330, as shown, or withany other element of the controller 1300. Although shown as a singleunit, the user interface 1350 may include one or more physical units.For example, the user interface 1350 may include an audio interface forperforming audio communication with a person, and a touch display forperforming visual and touch based communication with the person. In someembodiments, the user interface 1350 may include multiple displays, suchas multiple physically separate units, multiple defined portions withina single physical unit, or a combination thereof.

The sensor 1360 may include one or more sensors, such as an array ofsensors, which may be operable to provide information that may be usedto control the vehicle. The sensors 1360 may provide informationregarding current operating characteristics of the vehicle. The sensors1360 can include, for example, a speed sensor, acceleration sensors, asteering angle sensor, traction-related sensors, braking-relatedsensors, steering wheel position sensors, eye tracking sensors, seatingposition sensors, or any sensor, or combination of sensors, that isoperable to report information regarding some aspect of the currentdynamic situation of the vehicle 1000.

In some embodiments, the sensors 1360 may include sensors that areoperable to obtain information regarding the physical environmentsurrounding the vehicle 1000. For example, one or more sensors maydetect road geometry and obstacles, such as fixed obstacles, vehicles,and pedestrians. In some embodiments, the sensors 1360 can be or includeone or more video cameras, laser-sensing systems, infrared-sensingsystems, acoustic-sensing systems, or any other suitable type ofon-vehicle environmental sensing device, or combination of devices, nowknown or later developed. In some embodiments, the sensors 1360 and thelocation unit 1310 may be combined.

Although not shown separately, in some embodiments, the vehicle 1000 mayinclude a trajectory controller. For example, the controller 1300 mayinclude the trajectory controller. The trajectory controller may beoperable to obtain information describing a current state of the vehicle1000 and a rout planned for the vehicle 1000, and, based on thisinformation, to determine and optimize a trajectory for the vehicle1000. In some embodiments, the trajectory controller may output signalsoperable to control the vehicle 1000 such that the vehicle 1000 followsthe trajectory that is determined by the trajectory controller. Forexample, the output of the trajectory controller can be an optimizedtrajectory that may be supplied to the powertrain 1200, the wheels 1400,or both. In some embodiments, the optimized trajectory can be controlinputs such as a set of steering angles, with each steering anglecorresponding to a point in time or a position. In some embodiments, theoptimized trajectory can be one or more paths, lines, curves, or acombination thereof.

One or more of the wheels 1400 may be a steered wheel, which may bepivoted to a steering angle under control of the steering unit 1230, apropelled wheel, which may be torqued to propel the vehicle 1000 undercontrol of the transmission 1220, or a steered and propelled wheel thatmay steer and propel the vehicle 1000.

Although not shown in FIG. 1, a vehicle may include units, or elementsnot shown in FIG. 1, such as an enclosure, a Bluetooth® module, afrequency modulated (FM) radio unit, a Near Field Communication (NFC)module, a liquid crystal display (LCD) display unit, an organiclight-emitting diode (OLED) display unit, a speaker, or any combinationthereof.

FIG. 2 is a diagram of an example of a portion of a vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented. The vehicletransportation and communication system 2000 may include one or morevehicles 2100/2110, such as the vehicle 1000 shown in FIG. 1, which maytravel via one or more portions of one or more vehicle transportationnetworks 2200, and may communicate via one or more electroniccommunication networks 2300. Although not explicitly shown in FIG. 2, avehicle may traverse an area that is not expressly or completelyincluded in a vehicle transportation network, such as an off-road area.

In some embodiments, the electronic communication network 2300 may be,for example, a multiple access system and may provide for communication,such as voice communication, data communication, video communication,messaging communication, or a combination thereof, between the vehicle2100/2110 and one or more communication devices 2400. For example, avehicle 2100/2110 may receive information, such as informationrepresenting the vehicle transportation network 2200, from acommunication device 2400 via the network 2300.

In some embodiments, a vehicle 2100/2110 may communicate via a wiredcommunication link (not shown), a wireless communication link2310/2320/2370, or a combination of any number of wired or wirelesscommunication links. For example, as shown, a vehicle 2100/2110 maycommunicate via a terrestrial wireless communication link 2310, via anon-terrestrial wireless communication link 2320, or via a combinationthereof. In some implementations, a terrestrial wireless communicationlink 2310 may include an Ethernet link, a serial link, a Bluetooth link,an infrared (IR) link, an ultraviolet (UV) link, or any link capable ofproviding for electronic communication.

In some embodiments, a vehicle 2100/2110 may communicate with anothervehicle 2100/2110. For example, a host, or subject, vehicle (HV) 2100may receive one or more automated inter-vehicle messages, such as abasic safety message, from a remote, or target, vehicle (RV) 2110, via adirect communication link 2370, or via a network 2300. For example, theremote vehicle 2110 may broadcast the message to host vehicles within adefined broadcast range, such as 300 meters. In some embodiments, thehost vehicle 2100 may receive a message via a third party, such as asignal repeater (not shown) or another remote vehicle (not shown). Insome embodiments, a vehicle 2100/2110 may transmit one or more automatedinter-vehicle messages periodically, such as based on a definedinterval, such as 100 milliseconds.

Automated inter-vehicle messages may include vehicle identificationinformation, geospatial state information, such as longitude, latitude,or elevation information, geospatial location accuracy information,kinematic state information, such as vehicle acceleration information,yaw rate information, speed information, vehicle heading information,braking system status information, throttle information, steering wheelangle information, or vehicle routing information, or vehicle operatingstate information, such as vehicle size information, headlight stateinformation, turn signal information, wiper status information,transmission information, or any other information, or combination ofinformation, relevant to the transmitting vehicle state. For example,transmission state information may indicate whether the transmittingvehicle is in a neutral state, a parked state, a forward state, or areverse state.

In some embodiments, a vehicle 2100 may communicate with thecommunications network 2300 via an access point 2330. An access point2330, which may include a computing device, may be configured tocommunicate with a vehicle 2100, with a communication network 2300, withone or more communication devices 2400, or with a combination thereofvia wired or wireless communication links 2310/2340. For example, anaccess point 2330 may be a base station, a base transceiver station(BTS), a Node-B, an enhanced Node-B (eNode-B), a Home Node-B (HNode-B),a wireless router, a wired router, a hub, a relay, a switch, or anysimilar wired or wireless device. Although shown as a single unit, anaccess point may include any number of interconnected elements. Althoughnot shown separately in FIG. 2, in some embodiments, a vehicle 2100/2110may communicate with an element of the vehicle transportation network,such as an infrastructure device, via a direct communication link, whichmay be similar to the direct communication link 2370 shown in FIG. 2.For example, an infrastructure element of the vehicle transportationnetwork may include a communication device, such as communication device2400, and a vehicle 2100/2110 may communicate with the infrastructuredevice via a direct communication link, such as a Dedicated Short RangeCommunication (DSRC) protocol link, or any other direct communicationlink.

In some embodiments, the vehicle 2100 may communicate with thecommunications network 2300 via a satellite 2350, or othernon-terrestrial communication device. A satellite 2350, which mayinclude a computing device, may be configured to communicate with avehicle 2100, with a communication network 2300, with one or morecommunication devices 2400, or with a combination thereof via one ormore communication links 2320/2360. Although shown as a single unit, asatellite may include any number of interconnected elements.

An electronic communication network 2300 may be any type of networkconfigured to provide for voice, data, or any other type of electroniccommunication. For example, the electronic communication network 2300may include a local area network (LAN), a wide area network (WAN), avirtual private network (VPN), a mobile or cellular telephone network,the Internet, or any other electronic communication system. Theelectronic communication network 2300 may use a communication protocol,such as the transmission control protocol (TCP), the user datagramprotocol (UDP), the internet protocol (IP), the real-time transportprotocol (RTP) the Hyper Text Transport Protocol (HTTP), or acombination thereof. Although shown as a single unit, an electroniccommunication network may include any number of interconnected elements.

In some embodiments, a vehicle 2100 may identify a portion or conditionof the vehicle transportation network 2200. For example, the vehicle mayinclude one or more on-vehicle sensors 2105, such as sensor 1360 shownin FIG. 1, which may include a speed sensor, a wheel speed sensor, acamera, a gyroscope, an optical sensor, a laser sensor, a radar sensor,a sonic sensor, or any other sensor or device or combination thereofcapable of determining or identifying a portion or condition of thevehicle transportation network 2200.

In some embodiments, a vehicle 2100 may traverse a portion or portionsof one or more vehicle transportation networks 2200 using informationcommunicated via the network 2300, such as information representing thevehicle transportation network 2200, information identified by one ormore on-vehicle sensors 2105, or a combination thereof.

Although, for simplicity, FIG. 2 shows one vehicle 2100, one vehicletransportation network 2200, one electronic communication network 2300,and one communication device 2400, any number of vehicles, networks, orcomputing devices may be used. In some embodiments, the vehicletransportation and communication system 2000 may include devices, units,or elements not shown in FIG. 2. Although the vehicle 2100 is shown as asingle unit, a vehicle may include any number of interconnectedelements.

Although the vehicle 2100 is shown communicating with the communicationdevice 2400 via the network 2300, the vehicle 2100 may communicate withthe communication device 2400 via any number of direct or indirectcommunication links. For example, the vehicle 2100 may communicate withthe communication device 2400 via a direct communication link, such as aBluetooth communication link.

FIGS. 3-6 show examples of portions of a vehicle transportation network.For simplicity and clarity, unless otherwise specified, the vehicletransportation network portions are shown oriented with North at the topand East at the right.

FIG. 3 is a diagram of a portion of a vehicle transportation network inaccordance with this disclosure. A vehicle transportation network 3000may include one or more unnavigable areas 3100, such as a building, oneor more partially navigable areas, such as parking area 3200, one ormore navigable areas, such as roads 3300/3400, or a combination thereof.In some embodiments, a vehicle 3500, such as the vehicle 1000 shown inFIG. 1 or the vehicle 2100 shown in FIG. 2, which may be an autonomousvehicle, may traverse a portion or portions of the vehicletransportation network 3000. In some embodiments, the parking area 3200may include parking slots 3210.

A portion of the vehicle transportation network, such as a road3300/3400 may include one or more lanes 3320/3340/3360/3420/3440, andmay be associated with one or more directions of travel, which areindicated by arrows in FIG. 3.

The vehicle transportation network may include one or more intersections3600 or interchanges 3610 between one or more navigable, or partiallynavigable, areas 3200/3300/3400. For example, the portion of the vehicletransportation network shown in FIG. 3 includes an intersection 3600between the road 3300 and the road 3400. In another example, the portionof the vehicle transportation network shown in FIG. 3 includes aninterchange 3610 between the parking area 3200 and road 3400. Althoughnot shown in FIG. 3, an intersection 3600 or interchange 3610 mayinclude a traffic control device, such as a traffic light.

In some embodiments, a vehicle transportation network, or a portionthereof, such as the portion of the vehicle transportation network shownin FIG. 3, may be represented as vehicle transportation networkinformation. For example, vehicle transportation network information maybe expressed as a hierarchy of elements, such as markup languageelements, which may be stored in a database or file. For simplicity, theFigures herein depict vehicle transportation network informationrepresenting portions of a vehicle transportation network as diagrams ormaps; however, vehicle transportation network information may beexpressed in any computer-usable form capable of representing a vehicletransportation network, or a portion thereof. In some embodiments, thevehicle transportation network information may include vehicletransportation network control information, such as direction of travelinformation, speed limit information, toll information, gradeinformation, such as inclination or angle information, surface materialinformation, aesthetic information, or a combination thereof.

In some embodiments, the vehicle transportation network may beassociated with, or may include, a pedestrian transportation network.For example, FIG. 3 includes a portion 3700 of a pedestriantransportation network, which may be a pedestrian walkway. In someembodiments, a pedestrian transportation network, or a portion thereof,such as the portion 3700 of the pedestrian transportation network shownin FIG. 3, may be represented as pedestrian transportation networkinformation. In some embodiments, the vehicle transportation networkinformation may include pedestrian transportation network information. Apedestrian transportation network may include pedestrian navigableareas. A pedestrian navigable area, such as a pedestrian walkway or asidewalk, may correspond with a non-navigable area of a vehicletransportation network. Although not shown separately in FIG. 3, apedestrian navigable area, such as a pedestrian crosswalk, maycorrespond with a navigable area, or a partially navigable area, of avehicle transportation network.

FIG. 4 is a diagram of a portion of a vehicle transportation networkincluding an intersection 4000 in accordance with this disclosure. Avehicle transportation network intersection 4000 may include one or moreroads, each of which may include one or more lanes4100/4102/4104/4106/4110/41204122/4130/4132. One or more vehicles4200/4210 may traverse the vehicle transportation network via theintersection 4000. In FIG. 4, expected paths 4300/4310 for respectivevehicles are shown using directed broken lines.

In some embodiments, the flow of vehicles 4200/4210 through anintersection 4000 may be directed by one or more traffic control devices4400/4410/4420, such as one or more traffic lights or pedestriansignals. The traffic control devices 4400/4410/4420 of an intersectionmay be controlled by one or more traffic control device controllers (notshown). In some embodiments, a portion of a lane, or lanes, which mayinclude vehicle lanes, pedestrian lanes, or both, leading into anintersection 4000 may be identified as an approach. In some embodiments,the flow of vehicles 4200/4210 through an intersection 4000 via anapproach, or group of approaches, may be directed by a correspondingtraffic control device 4400/4410/4420. A movement may represent atraversal of an expected, or permitted, path through the intersection bya vehicle in a lane. Although not shown in FIG. 4, in some embodiments,an intersection may include one or more non-vehicle approaches, such aspedestrian approaches.

In some embodiments, the approaches directed by a traffic control device4400/4410/4420 may be identified as a signal group. For example, in FIG.4, the rightmost northbound lane 4100 may be directed by traffic controldevice 4400, the next the rightmost northbound lanes 4102/4104 may be asignal group and may be directed by traffic control device 4410, and theleftmost northbound lane 4106 may be directed by traffic control device4420. In some embodiments, each signal group may be associated with anidentifier or label.

In some embodiments, the traffic control device controller may directtraffic through the intersection using one or more phases or blocks. Aphase, or block, may include an allocation of permitted right-of-waydirection, such as a green signal or a walk signal, to one or moresignal groups, or movements, concurrently. A green phase, or block, mayrepresent one or more signal groups concurrently having a proceed, orpermitted, rite-of-way indicator, such as a green signal or a walksignal. A red phase, or block, may represent one or more signal groupsconcurrently having a do-not-proceed or denied rite-of-way indicator,such as a red signal or a stop signal.

In some embodiments, phases may be scheduled to minimize or eliminateinter-signal group conflicts. An inter-signal group conflict mayindicate that the expected path for vehicles, or pedestrians, in a laneor approach of a signal group overlaps with the movement of a lane orapproach of another signal group of the vehicle transportation networkintersection 4000. The geographic location of the overlap of theexpected paths 4300/4310 of a conflict may be identified as the conflictarea 4500. For example, the expected path 4300 of the vehicle 4200 shownat the lower right conflicts with the expected path 4310 of the vehicle4210 shown at the center left at the conflict area 4500. In someembodiments, a green phase may include one or more conflictingmovements. For example, the expected path of a pedestrian crossing alane may conflict with the expected path of a vehicle turning onto thelane, and a phase may include signal groups including the pedestrianpath and the vehicle path. A conflicting expected path in a green phasemay be a permitted expected path, or permitted movement, and anon-conflicting movement in a green phase may be a protected movement.

In some embodiments, a cycle may include a green phase corresponding toeach of the signal groups of an intersection, and the cycle time may bethe sum of the period or duration of the phases of the cycle. The signalcycle for a signal group may indicate the sum of the period or durationof each possible state, such as proceed or green, proceed with cautionor yellow, do-not-proceed or red, of the signal group. An interval mayindicate the duration or period of each respective signal state for asignal group. Each interval may be associated with a respective definedminimum duration, defined maximum duration, or both. In someembodiments, a yellow, or warning, phase may be included. The yellowphase may be a defined duration, such as four or five seconds.

In some embodiments, the control of the traffic control devices for anintersection may be based on one or more defined schedules and may omitusing current traffic state information. In some embodiments, thecontrol of the traffic control devices for an intersection may be basedon vehicle actuation. For example, the traffic control device controllermay receive information indicating a current traffic state, such as thearrival of a vehicle in an approach to the intersection, and may adjustthe phases of the intersection based on the current traffic information.For example, the intersection may include one or more vehicle detectiondevices, or loops, corresponding to one or more approaches to theintersection, which may detect vehicles geographically located withinthe respective approach. In some embodiments, an approach may includemultiple detection devices. In some embodiments, vehicle-actuatedcontrol may determine whether to extend, or continue, a current phasebased on the traffic state of approaches corresponding to the currentgreen phase and may omit using current traffic state informationcorresponding to other approaches. In some embodiments, adaptive controlmay determine whether to extend, or continue, a current phase based onthe traffic state of approaches corresponding to the current greenphase, the other approaches, or a combination thereof.

FIG. 5 is another diagram of the portion of the vehicle transportationnetwork intersection 4000 shown in FIG. 4 in accordance with thisdisclosure. As shown in FIG. 5, a vehicle transportation networkintersection may include multiple movements. For example, vehicles inthe right-most northbound lane may precede strait across theintersection 4000 or may turn right through the intersection 4000, asshown at 5100. Vehicles in the two center northbound lanes may precedestrait across the intersection 4000 as shown at 5110 and 5120. Vehiclesin the left-most northbound lane may turn left through the intersection4000 as shown at 5130. Vehicles in the right southbound lane may turnleft through the intersection 4000 as shown at 5200. Vehicles in theleft southbound lane may precede strait across the intersection 4000 ormay turn right through the intersection 4000 as shown at 5210. Vehiclesin the top westbound lane may precede strait across the intersection4000 or may turn right through the intersection 4000 as shown at 5300.Vehicles in the center westbound lane may precede strait across theintersection 4000 as shown at 5310. Vehicles in the bottom westboundlane may turn left through the intersection 4000 as shown at 5320.Vehicles in the top eastbound lane may turn left through theintersection 4000 as shown at 5400. Vehicles in the center eastboundlane may precede strait across the intersection 4000 as shown at 5410.Vehicles in the bottom eastbound lane may precede strait across theintersection 4000 or may turn right through the intersection 4000 asshown at 5420.

One or more of the movements 5100-5420 may be included in a phase orblock. For example, a cycle for the intersection 4000 may include fourphases. A first phase may include the right-most northbound movement5100, which may include the northbound right turn, the center northboundmovements 5110 and 5120, and the southbound straight and right turnmovement 5210. A second phase may include the northbound left turn 5130and southbound left turn 5200. A third phase may include the topwestbound movement 5300, the center westbound movement 5310, the centereastbound movement 5410, and the bottom eastbound movement 5420. Afourth phase may include the bottom westbound movement 5320 and the topeastbound movement 5400.

FIG. 6 is another diagram of the portion of the vehicle transportationnetwork intersection 4000 shown in FIG. 4 in accordance with thisdisclosure. As shown in FIG. 6, a vehicle transportation networkintersection may include one or more detectors 6000/6100/6200, ordetection loops. A detector may include any infrastructure device, orcombination of devices, capable of detecting vehicles in one or moreapproaches to the intersection, such as vehicles arriving at theintersection. For example, a detection loop may be a pressure sensorembedded in the roadway.

In some embodiments, the vehicle transportation network intersection mayinclude a detector, or multiple detectors, for detecting vehicles inmultiple locations within an approach. For example, a lane of anapproach of an intersection may include multiple detectors. In someembodiments, an approach, or a lane of an approach, may include aproximal loop detector proximate to the point of convergence of theapproach to the intersection as shown at 6000, a distal loop detectorgeographically distant from the intersection, such as 100 meters or 300meters from the intersection, as shown at 6100, an intermediate loopdetector geographically between the proximate loop detector and thedistal loop detector relative to the intersection as shown at 6200, orany combination thereof. Although three detectors are shown in eachnorthbound lane in FIG. 6, any number of detectors may be used in anintersection. For example, a lane may include a single detector, or mayinclude five detectors. In another example, each lane, or each approach,to the intersection may include one or more detectors.

FIG. 7 is a diagram of an example of a method of traversing a vehicletransportation network using expected traffic control device stateinformation in accordance with this disclosure. In some embodiments,traversing a vehicle transportation network using expected trafficcontrol device state information may be implemented in a vehicle, suchas the vehicle 1000 shown in FIG. 1 or the vehicles 2100/2110 shown inFIG. 2.

In some embodiments, traversing a vehicle transportation network usingexpected traffic control device state information may includeidentifying vehicle transportation network information at 7000,identifying a route at 7100, traversing the vehicle transportationnetwork at 7200, or a combination thereof.

In some embodiments, vehicle transportation network information may beidentified at 7000. For example, a vehicle, such as the vehicle 1000shown in FIG. 1 or the vehicles 2100/2110 shown in FIG. 2, may read thevehicle transportation network information from a data storage unit,such as the memory 1340 shown in FIG. 1, or may receive the vehicletransportation network information from an external data source, such asthe communicating device 2400 shown in FIG. 2, via a communicationsystem, such as the electronic communication network 2300 shown in FIG.2. In some embodiments, identifying the vehicle transportation networkinformation may include transcoding or reformatting the vehicletransportation network information, storing the reformatted vehicletransportation network information, or both.

In some embodiments, the vehicle transportation network information mayinclude expected traffic control device state information. In someembodiments, the expected traffic control device state information mayinclude, for example, a machine learning model, such as an artificialneural network model or a support vector regression model. In someembodiments, the model may be trained to determine an expected trafficcontrol device state for one or more traffic control devices of anintersection of the vehicle transportation network. In some embodiments,the expected traffic control device state information may be informationdetermined using an expected traffic control device state determinationunit. For example, an expected traffic control device statedetermination unit may be implemented by a processor, such as theprocessor 1330 of the vehicle 1000 shown in FIG. 1, or a processor of anexternal device, such as the communicating device 2400 shown in FIG. 2.In some embodiments, the expected traffic control device statedetermination unit may implement a machine learning algorithm and maytrain the machine learning algorithm based on training data, such asshown in FIG. 8.

In some embodiments, as shown in FIG. 7, a route may be identified at7100. In some embodiments, identifying a route at 7100 may includeidentifying an origin, such as a current geospatial and temporallocation of the vehicle, a destination, which may be a geospatiallocation identified in the vehicle transportation network, or both. Insome embodiments, a route from the origin to the destination in thevehicle transportation network may be identified at 7100 using thetransportation network information identified at 7000. In someembodiments, the route may include a vehicle transportation networkintersection, the expected traffic control device state informationidentified at 7000 may include expected traffic control device stateinformation corresponding to the vehicle transportation networkintersection, and identifying the route at 7100 may include using theexpected traffic control device state information corresponding to thevehicle transportation network intersection identified at 7000, as shownin FIG. 9.

In some embodiments, as shown in FIG. 7, the vehicle may traverse thevehicle transportation network at 7200 using the route identified at7100. For example, in some embodiments, the vehicle may be an autonomousvehicle and a trajectory controller, such as the trajectory controller1300 shown in FIG. 1, may operate the vehicle to traverse the vehicletransportation network from the origin to the destination using theexpected traffic control device state information identified at 7000,the route identified at 7100, or both, as shown in FIG. 10.

FIG. 8 is a diagram of an example of a method of identifying expectedtraffic control device state information in accordance with thisdisclosure. In some embodiments, generating expected traffic controldevice state information may be implemented in a vehicle, such as thevehicle 1000 shown in FIG. 1 or the vehicles 2100/2110 shown in FIG. 2.

In some embodiments, identifying expected traffic control device stateinformation at 7000 may include receiving traffic control information at8000, identifying candidate features at 8100, determining selectedfeatures at 8200, normalizing training data at 8300, grouping featuresat 8400, or a combination thereof.

In some embodiments, traffic control information may be received at8000. In some embodiments, infrastructure components of the vehicletransportation network corresponding to an intersection, such as atraffic control device controller, may generate and store trafficinformation corresponding to the intersection. In some embodiments, thetraffic information may represent discrete events, such as the detectionof a vehicle, or the changing of a phase, for the intersection. Forexample, the traffic control device controller may be a vehicle actuatedtraffic control device controller, and may generate and store trafficinformation corresponding to the traffic detected in and the operationalcontrol of the intersection.

In some embodiments, the traffic control information may be formatted asbinary data. For example, the traffic information may include binarydata representing detector loops, internal signal parameters, externalsignal parameters, speed detection, or any other information detected byor determined by the infrastructure components. In some embodiments, thetraffic information may be recorded periodically, such as every 0.1seconds, or at any other interval. Although the traffic controlinformation is described herein as being formatted as binary data, thetraffic control information may include data in any data format, orcombination of formats.

In some embodiments, candidate features may be identified at 8100. Forexample, identifying candidate features at 8100 may include convertingthe binary formatted traffic control information representing discreteevents into features that describe the operation of the traffic controldevice controller as continuous data, such as data including countdowns,timers, cumulative vehicle counts, or the like.

In some embodiments, identifying candidate features, or featureextraction, may include using defined initiation data, such as thetraffic control information identified at 8000, and may include derivingfeatures based on the initiation data. In some embodiments, theinitiation data may include redundant or low value data. In someembodiments, identifying candidate features at 8100 may includeextracting data from the initiation data that informative,non-redundant, and useful for training the machine learning algorithm togenerate an efficient and effective model.

In some embodiments, the binary formatted traffic control informationmay include, for example, a matrix of data in which rows indicate timeand columns represent information used as input or output of thecontroller. For example, the information at row i and column j mayindicate the value of the control jth control parameter at the ith time.In some embodiments, the aggregation level, or interval, of time may be1/10th of a second, which may correspond with the frequency at which thetraffic control information is generated and stored.

In some embodiments, identifying candidate features at 8100 may includegenerating cumulative or aggregate information. For example, the vehiclecontrol information may indicate whether a vehicle is detected during adiscrete detection period, but may omit information indicating acumulative or aggregate count of vehicles detected during an aggregateperiod, such as a during the duration of a phase or a cycle, andidentifying candidate features at 8100 may include generating acumulative or aggregate count of vehicles detected during an aggregateperiod, such as a during the duration of a phase or a cycle. In someembodiments, one or more features may be identified in response toinput, such as user input indicating the features.

In some embodiments, a feature may represent the behavior of a signalgroup and may correspond to an event or an aggregation of events fromthe traffic information. In some embodiments, the features identifiedmay include indexing features, detection features, external controlstate features, internal control state features, or any combinationthereof.

An indexing feature may represent some or all of the signal groups foran intersection. For example, the traffic information may indicate atemporal location for corresponding information, which may include adate and a time, and the date may be identified as a featurerepresentative of traffic information.

In some embodiments, detector features may be based on vehicle detectionin one or more approaches of a signal group. In some embodiments,feature extraction may include identifying features based on theproximate loop detector and the distal loop detector trafficinformation. In some embodiments, detection features may includeaggregate features, such as an aggregate count, or cardinality of theset, of vehicles detected in the approach at a discrete time, anaggregate count of vehicles detected in the approach during a discretephase, or an aggregate count of vehicles detected in the approach duringa consecutive sequence of cycles, such as five cycles, immediatelypreceding a discrete time.

In some embodiments, external control state features may describecontrol signal output of the controller. For example, the externalcontrol state features may represent proceed, or green, signal timeperiods, do-not-proceed, or red, signal time periods, or the like. Insome embodiments, external control state features may represent externalcontrol state for a signal group at a discrete time, or during aconsecutive sequence of cycles, such as five cycles, immediatelypreceding a discrete time. The external control state features mayinclude, for example, timers, countdowns, proximal phase durations, orthe like. A timer may represent an amount of time passed, or expired,for a current phase, which may be the phase corresponding to a discretetime. A countdown may indicate an expected remaining duration for acurrent phase. The proximal phase durations may represent the durationof the phases, such as the red phases and green phases, of a consecutivesequence of cycles, such as five cycles, immediately preceding adiscrete time.

In some embodiments, internal control state features may representcontrol levels, such as a group completion control level or a signalgroup completion control level, within the controller. The groupcompletion control level may correspond with the allocation of a greenphase to a signal group. The Signal group completion control level maycorrespond with the duration of the current green phase. The internalcontrol state features may include, for example, a timer indicatingelapsed time of a current cycle relative to a signal group. In anotherexample, the internal control state features may include an indicationof a realization method, such as primary or alternative realization, fora green phase, which may include a timer, a countdown, and a definedtemporal length, or duration, for each realization method.

In some embodiments, multiple features may be extracted per signalgroup. Features extracted may include elapsed permitted right-of-waysignal temporal information, which may include an amount of time, suchas a number of seconds, that has passed since the beginning of a greenphase relative to time the information was generated. Features extractedmay include elapsed signal phase temporal information, which may includean amount of time, such as a number of seconds, that has passed sincethe beginning of a signal phase current relative to time the informationwas generated. Features extracted may include elapsed cycle temporalinformation, which may include an amount of time, such as a number ofseconds, that has passed since the beginning of a cycle current relativeto time the information was generated. Features extracted may include amaximum lane-wise cardinality of detected vehicles corresponding to theelapsed permitted right-of-way signal temporal information, which mayindicate a maximum count, or cardinality, of vehicles detected in a lanecontrolled by a green signal, relative to time the information wasgenerated.

In some embodiments, features extracted may include permittedright-of-way signal durations associated with a current phase relativeto time the information was generated. For example, the permittedright-of-way signal durations may correspond with a sequence ofpermitted right-of-way signals for the signal group preceding thecurrent right-of-way signal for the signal group, such as the precedingfive permitted right-of-way signals.

In some embodiments, features extracted may include prior maximumpermitted lane-wise cardinalities. Each prior maximum permittedlane-wise cardinality may indicate a maximum permitted lane-wisecardinality of detected vehicles corresponding to a permittedright-of-way signal duration from sequence of permitted right-of-waysignals preceding the current permitted right-of-way signal for thesignal group relative to time the information was generated.

In some embodiments, features extracted may include denied right-of-waysignal durations associated with a current phase relative to time theinformation was generated. For example, the denied right-of-way signaldurations may correspond with a sequence of denied right-of-way for thesignal group preceding the current right-of-way signal for the signalgroup, such as the preceding five denied right-of-way signals.

In some embodiments, features extracted may include prior maximum deniedlane-wise cardinalities. Each prior maximum denied lane-wise cardinalitymay indicate a maximum denied lane-wise cardinality of detected vehiclescorresponding to a denied right-of-way signal duration from sequence ofdenied right-of-way signals preceding the current denied right-of-waysignal for the signal group relative to time the information wasgenerated.

In some embodiments, features extracted may include a signal durationextension factor indicator indicating that a traffic control deviceright-of-way signal duration for a traffic control device from a firstsignal group, corresponding to the current right-of-way signal for thesignal group, was extended in response to traffic control temporalinformation for a second signal group.

In some embodiments, an intersection may include G signal groups, suchas ten signal groups (G=10), f features, such as 112 features (f=112),may be extracted per signal group, and 1120 features (10*112=1120) maybe extracted for the intersection. For example, Table 1, below, shows anexample of candidate features extracted from traffic controlinformation.

TABLE 1 INDEX FEATURE NAME DESCRIPTION 1 date Date of the logged data inthe dataset 2 index Row index 3 X10th_sec 1/10th of a second index, eachrow indicates 1/10 of a second 4 R_Time Timer of current red signal 5R_CD Countdown of current red signal 6 R_Length Absolute length in timeof current red phase 7 R_Length1 Absolute length in time of one redphase before current red phase 8 R_Length2 Absolute length in time ofthe second red phase before current red phase 9 R_Length3 Absolutelength in time of the third red phase before current red phase 10R_Length4 Absolute length in time of the fourth red phase before currentred phase 11 R_Length5 Absolute length in time of the fifth red phasebefore current red phase 12 G_Time Timer of current green signal 13 G_CDCountdown of current green signal 14 G_Length Absolute length in time ofcurrent green phase 15 G_Length1 Absolute length in time of one greenphase before current green phase 16 G_Length2 Absolute length in time ofthe second green phase before current green phase 17 G_Length3 Absolutelength in time of the third green phase before current green phase 18G_Length4 Absolute length in time of the fourth green phase beforecurrent green phase 19 G_Length5 Absolute length in time of the fifthgreen phase before current green phase 20 Y_Time Timer of current yellowsignal 21 Y_CD Countdown of current yellow signal 22 Y_Length Absolutelength in time of current yellow phase 23 Block2Block_Time Timer ofcurrent block and the corresponding block in the next cycle 24Block2Block_CD Countdown of current block and the corresponding block inthe next cycle 25 Block2Block_Length Absolute length in time of currentblock and the corresponding block in the next cycle 26 Block_Time Timerof start to end of current block 27 Block_CD Countdown of start to endof current block 28 Block_Length Absolute length in time of start to endof current block 29 InterCycleTime_cnt The summation of green phases ofthe signal groups of a cycle of the intersection. This variable givesthe elapsed time within the summation of passed green phases 30InterCycleTime_cnt1 InterCycleTime_cnt of one cycle before current cycle31 InterCycleTime_cnt2 InterCycleTime_cnt of second cycle before currentcycle 32 InterCycleTime_cnt3 InterCycleTime_cnt of third cycle beforecurrent cycle 33 InterCycleTime_cnt4 InterCycleTime_cnt of fourth cyclebefore current cycle 34 InterCycleTime_cnt5 InterCycleTime_cnt of fifthcycle before current cycle 35 InterCycleNumb_cnt Number of green phasesthat are summed in InterCycleTime_cnt 36 InterCycleNumb_cnt1 Number ofgreen phases that are summed in InterCycleTime_cnt1 37InterCycleNumb_cnt2 Number of green phases that are summed inInterCycleTime_cnt2 38 InterCycleNumb_cnt3 Number of green phases thatare summed in InterCycleTime_cnt3 39 InterCycleNumb_cnt4 Number of greenphases that are summed in InterCycleTime_cnt4 40 InterCycleNumb_cnt5Number of green phases that are summed in InterCycleTime_cnt5 41SCycle_Time Timer of the start to end of cycle of that particular signalgroup 42 SCycle_CD Countdown of the start to end of cycle of thatparticular signal group 43 SCycle_Length Absolute length in time of thestart to end of cycle of that particular signal group 44 AR_Value Binaryindication of alternative realization 45 AR_Time Timer if AR_Value == 146 AR_CD Countdown if AR_Value == 1 47 AR_Length Absolute length in timeof alternative realization from start to end if AR_Value == 1 48Aan_Active Binary indication of ‘aanvraag’ request parameter 49 Aan_TimeTimer if Aan_Active == 1 50 Aan_CD Countdown if Aan_Active == 1 51Aan_Length Absolute length in time of request parameter from start toend if AR_Value == 1 52 CG_Value Categorical value of CG value(realization method) 53 RA_Active Binary indication of ‘red beforegreen’ parameter 54 RA_Time Timer if RA_Active == 1 55 RA_CD Countdownif RA_Active == 1 56 RA_Length Absolute length in time of start to endof RA_active parameter if FG_Active == 1 57 FG_Active Binary indicationwhen fixed green is active 58 FG_Time Timer if FG_Active == 1 59 FG_CDCountdown if FG_Active == 1 60 FG_Length Absolute length in time ofstart to end of FG_active parameter is FG_active == 1 61 WG_ActiveBinary indication when waiting green is active 62 WG_Time Timer ifWG_Active == 1 63 WG_CD Countdown if WG_Active == 1 64 WG_LengthAbsolute length in time of start to end of WG_active parameter isWG_active == 1 65 VG_Active Binary indication when extending green isactive 66 VG_Time Timer if VG_Active == 1 67 VG_CD Countdown ifVG_Active == 1 68 VG_Length Absolute length in time of start to end ofVG_active parameter is VG_active == 1 69 MG_Active Binary indicationwhen prolonging green is active 70 MG_Time Timer if MG_Active == 1 71MG_CD Countdown if MG_Active == 1 72 MG_Length Absolute length in timeof start to end of MG_active parameter is MG_active == 1 73 RV_ActiveBinary indication of red before request parameter 74 RV_Time Timer ifRV_Active == 1 75 RV_CD Countdown if RV_Active == 1 76 RV_LengthAbsolute length in time of start to end of RV_active parameter isRV_active == 1 77 AtTdet1_MaxVol Cumulative vehicle count at the firstdetector loop at current time 78 RDet1_MaxVol Cumulative vehicle countat the first detector loop at current time when output signal is red 79RDet1_MaxVol1 RDet1_MaxVol of one cycle before current cycle 80RDet1_MaxVol2 RDet1_MaxVol of second cycle before current cycle 81RDet1_MaxVol3 RDet1_MaxVol of third cycle before current cycle 82RDet1_MaxVol4 RDet1_MaxVol of fourth cycle before current cycle 83RDet1_MaxVol5 RDet1_MaxVol of fifth cycle before current cycle 84GDet1_MaxVol Cumulative vehicle count at the first detector loop atcurrent time when output signal is green 85 GDet1_MaxVol1 GDet1_MaxVolof one cycle before current cycle 86 GDet1_MaxVol2 GDet1_MaxVol ofsecond cycle before current cycle 87 GDet1_MaxVol3 GDet1_MaxVol of thirdcycle before current cycle 88 GDet1_MaxVol4 GDet1_MaxVol of fourth cyclebefore current cycle 89 GDet1_MaxVol5 GDet1_MaxVol of fifth cycle beforecurrent cycle 90 YDet1_MaxVol Cumulative vehicle count at the firstdetector loop at current time when output signal is yellow 91YDet1_MaxVol1 YDet1_MaxVol of one cycle before current cycle 92YDet1_MaxVol2 YDet1_MaxVol of second cycle before current cycle 93YDet1_MaxVol3 YDet1_MaxVol of third cycle before current cycle 94YDet1_MaxVol4 YDet1_MaxVol of fourth cycle before current cycle 95YDet1_MaxVol5 YDet1_MaxVol of fifth cycle before current cycle 96AtTdet3_MaxVol Cumulative vehicle count at the third detector loop atcurrent time 97 RDet3_MaxVol Cumulative vehicle count at the thirddetector loop at current time when output signal is red 98 RDet3_MaxVol1RDet3_MaxVol of one cycle before current cycle 99 RDet3_MaxVol2RDet3_MaxVol of second cycle before current cycle 100 RDet3_MaxVol3RDet3_MaxVol of third cycle before current cycle 101 RDet3_MaxVol4RDet3_MaxVol of fourth cycle before current cycle 102 RDet3_MaxVol5RDet3_MaxVol of fifth cycle before current cycle 103 GDet3_MaxVolCumulative vehicle count at the third detector loop at current time whenoutput signal is green 104 GDet3_MaxVol1 GDet3_MaxVol of one cyclebefore current cycle 105 GDet3_MaxVol2 GDet3_MaxVol of second cyclebefore current cycle 106 GDet3_MaxVol3 GDet3_MaxVol of third cyclebefore current cycle 107 GDet3_MaxVol4 GDet3_MaxVol of fourth cyclebefore current cycle 108 GDet3_MaxVol5 GDet3_MaxVol of fifth cyclebefore current cycle 109 YDet3_MaxVol Cumulative vehicle count at thethird detector loop at current time when output signal is yellow 110YDet3_MaxVol1 YDet3_MaxVol of one cycle before current cycle 111YDet3_MaxVol2 YDet3_MaxVol of second cycle before current cycle 112YDet3_MaxVol3 YDet3_MaxVol of third cycle before current cycle 113YDet3_MaxVol4 YDet3_MaxVol of fourth cycle before current cycle 114YDet3_MaxVol5 YDet3_MaxVol of fifth cycle before current cycle

In some embodiments, the features for a signal group, for a discretetime period, such as one day, may be stored in a discrete file, such asa comma separated variable (CSV) file. In some embodiments, the featuresmay be extracted periodically, such as at 0.1 second intervals, and theextracted features for a signal group for a day may include a matrixhaving columns representing features (Feature_(f)) and rows representingtime (T_(f)). In an example, the matrix for ten signal groups (G=10),112 features (f=112), and a two hour time period, which may include72000 rows (2*60*60*10=72000), may be expressed as the following:

$\begin{bmatrix}{T_{1}{\_ Feature}_{1}} & \ldots & {T_{1}{\_ Feature}_{112}} \\\vdots & \ddots & \vdots \\{T_{72000}{\_ Feature}_{1}} & \ldots & {T_{72000}{\_ Feature}_{112}}\end{bmatrix}.$

In some embodiments, the features per signal group per time period, suchas one day, may be column-wise binded, or concatenated, over the signalgroups of the intersection, which may be expressed as follows:

$\begin{bmatrix}{T_{1}{\_ Feature}_{1}{\_ SignalGroup}\; 1} & \ldots & {T_{1}{\_ Feature}_{112}{\_ SignalGroup}\; 11} \\\vdots & \ddots & \vdots \\{T_{72000}{\_ Feature}_{1}{\_ SignalGroup}\; 1} & \ldots & {T_{72000}{\_ Feature}_{112}{\_ SignalGroup}\; 11}\end{bmatrix}.$

In some embodiments, the column-wise binded features for the signalgroups of an intersection per time period, such as one day, may berow-wise binded over multiple periods, such as the days of a month, or30 consecutive days, which may be expressed as follows:

$\begin{bmatrix}{{Day}\; 1{\_ T}_{1}{\_ Feature}_{1}{\_ SignalGroup}\; 1} & \ldots & {{Day}\; 1{\_ T}_{1}{\_ Feature}_{112}{\_ SignalGroup}\; 11} \\\vdots & \ddots & \vdots \\{{Day}\; 30{\_ T}_{72000}{\_ Feature}_{1}{\_ SignalGroup}\; 1} & \ldots & {{Day}\; 30{\_ T}_{72000}{\_ Feature}_{112}{\_ SignalGroup}\; 11}\end{bmatrix}.$

In some embodiments, identifying the candidate features at 8100 mayinclude validating the extracted features to omit data based oninaccurate information, such as information generated by faultydetectors. In some embodiments, the traffic information reported by andused by the controller may include inaccurate, false, or misleadinginformation. For example, a faulty detector may inaccurately report adetected vehicle (false positive), and the controller may direct theflow of traffic through the intersection based on the inaccurate data,treating the false positives as accurate data.

In some embodiments, validating the extracted features may includeidentifying defined target features as quality indicators, datasegmentation, identifying defined temporal contexts, identifying highlyvariant data, or a combination thereof.

In some embodiments, defined target, or key, features may be identifiedas quality indicators based on one or more statistical metrics. One ormore defined target features may be identified based on each statisticalmetric. For example, a correlation statistical metric may be identifiedbased on a cumulative count of vehicles detected by a defined detector,such as the detector proximate to the intersection, for an approach andthe duration of the corresponding green phase. Based on the correlationstatistical metric a maximum cardinality, or volume, of vehicles perlane may be identified as a defined target feature, and a green phaseduration may be identified as a target feature. In another example, anarithmetic mean statistical metric, a maximum value statistical metric,a standard deviation statistical metric, or a combination thereof, maybe identified based on a cumulative count of vehicles detected by adefined detector, such as the detector proximate to the intersection,for an approach. Based on one or more of these statistical metrics amaximum cardinality, or volume, of vehicles per lane may be identifiedas a defined target feature. In another example, an arithmetic meanstatistical metric, a maximum value statistical metric, a standarddeviation statistical metric, or a combination thereof, may beidentified based on the green phase duration for an approach. Based onone or more of these statistical metrics a green phase duration may beidentified as a defined target feature. In another example, a maximumnumber of green phases during a defined period may be identified as adefined target feature. In another example, an arithmetic meanstatistical metric may be identified based a temporal length between thebeginning of a phase and the subsequent beginning of the phase for adominant signal group. Based on this metric, a signal cycle duration maybe identified as a defined target feature.

In some embodiments, data segmentation may include identifying abnormalpatterns in the data. Data segmentation may include evaluating the datacorresponding to each defined temporal period independently. Forexample, the data may be evaluated on a day by day basis or the day byday data during a defined period of each day, such as 7:00 a.m. to 9:00a.m.

In some embodiments, defined temporal contexts may be identified. Insome embodiments, identifying defined temporal contexts may includetemporally grouping feature values. In some embodiments, highly variantdata, or outliers, may be identified. In some embodiments, identifyingoutliers may include identifying one or more expected, or normal, valuesor value ranges for respective features, such as values corresponding toa stable arithmetic means for the feature. In some embodiments, theexpected values may be identified based on the defined temporalcontexts. For example, for a feature an expected value may be identifiedfor a weekday rush time period and another expected value may beidentified for a corresponding time period of a weekend. In someembodiments, identifying outliers may include identifying valuesexceeding a defined threshold, such as a confidence interval or rangearound an identified expected value for the feature, identifyingcorresponding standard deviations, and identifying a correlation for acorresponding defined temporal context or identified standard pattern,such as weekend and weekday, summer and winter, holiday and non-holiday,peak and non-peak, or the like.

In some embodiments, selected features may be identified at 8200. Insome embodiments, the features may be selected based on thoroughstatistical analysis methods. In some embodiments, the feature selectionmay include selecting features that have the strongest relation with atarget variable.

For example, selected features may be identified for a permitted, orgreen, right-of-way signal model, a denied, or red, right-of-way signalmodel, or both. In some embodiments, the features selected for apermitted right-of-way signal model may be based on featurescorresponding to the signal group associated with the correspondingpermitted right-of-way signal, which may include internal signal states,output signal states, loop detector data, or a combination thereof. Insome embodiments, the features selected for a permitted right-of-waysignal model may be based on, or weighted to prioritize, featuresrelated to detecting vehicles, such as detector loop features. In someembodiments, the features selected for a denied right-of-way signalmodel may be based on features corresponding any or all of the signalgroups of the intersection, which may include internal signal states,output signal states, loop detector data, or a combination thereof. Insome embodiments, the features selected for a denied right-of-way signalmodel may be based on, or weighted to prioritize, features related tocontroller state, or internal, features.

In some embodiments, training data may be normalized, or scaled, at8300. In some embodiments, normalizing, or scaling, the training datamay reduce operational complexity. For example, a support vectorregression model may include using a kernel, which may use a dotproduct, or inner product, of a vector of features, may include using alarge feature space, and normalizing the training data may reduceoperational complexity.

For example, a traffic control feature may indicate the elapsedpermitted right-of-way signal temporal information current relative totime the information was generated, and the corresponding feature valuemay be divided by a defined maximum permitted right-of-way signaltemporal value to generate a normalized value. In another example, thetraffic control feature may indicate elapsed signal phase temporalinformation current relative to time the information was generated, andthe corresponding feature value may be divided by a defined maximumpermitted right-of-way signal temporal value to generate a normalizedvalue. In another example, the traffic control feature may indicateelapsed cycle temporal information current relative to time theinformation was generated, and the corresponding feature value may bedivided by a defined maximum permitted right-of-way signal temporalvalue to generate a normalized value. In another example, the trafficcontrol feature may indicate a permitted right-of-way signal durationassociated with the elapsed permitted right-of-way signal temporalinformation current relative to time the information was generated, andthe corresponding feature value may be divided by a defined maximumpermitted right-of-way signal temporal value to generate a normalizedvalue. In another example, the traffic control feature may indicate adenied right-of-way signal duration associated with the elapsedpermitted right-of-way signal temporal information current relative totime the information was generated, and the corresponding feature valuemay be divided by a defined maximum denied right-of-way signal temporalvalue to generate a normalized value. In another example, the trafficcontrol feature may indicate a maximum lane-wise cardinality of detectedvehicles corresponding to the elapsed permitted right-of-way signaltemporal information current relative to time the information wasgenerated, and the corresponding feature value may be divided by adefined lane-wise maximum capacity to generate a normalized value. Inanother example, the traffic control feature may indicate a priormaximum permitted lane-wise cardinality current relative to time theinformation was generated, and the corresponding feature value may bedivided by a defined lane-wise maximum capacity to generate a normalizedvalue. In another example, the traffic control feature may indicate aprior maximum denied lane-wise cardinality current relative to time theinformation was generated, and the corresponding feature value may bedivided by a defined lane-wise maximum capacity to generate a normalizedvalue.

In some embodiments, the data may be normalized to a [0,1] scale. Insome embodiments, normalization may include unity-based normalization,which may be expressed as the following:

$X^{\prime} = {\frac{X - X_{m\; i\; n}}{X_{{ma}\; x} - X_{m\; i\; n}}.}$

In some embodiments, a standard score of a feature vector may be usedfor scaling. In some embodiments, the standard score may include using amean μ of the respective plurality of selected traffic control featurevalues, a standard deviation σ of the respective plurality of selectedtraffic control feature values, or both, which may be expressed as thefollowing:

$X^{\prime} = {\frac{X - \mu}{\sigma}.}$

In some embodiments, normalizing may include setting a restriction onhow scaling is applied on the total dataset versus a small subset. Insome embodiments, a feature vector of the total dataset may be referredto as the population.

In some embodiments, features may be grouped at 8400. For example, thenormalized selected features may be grouped temporally, such as inhourly periods, daily periods, or by weekday and weekend groups. Forexample, the normalized traffic control feature values of selectedtraffic control features that correspond temporally with a definedtemporal location, such as defined period of each day, such as 7:00 a.m.to 9:00 a.m. from each day of a five day period may be identified.

In some embodiments, data, such as the selected normalized data, may beused to train a machine learning algorithm, such as an artificial neuralnetwork or a support vector regression algorithm, at 8500. Training amachine learning algorithm may include creating or training a model,which may include classifying, or categorizing, elements from thetraining data. Each of the features selected at 8200 may be a dimensionused for training the model. In some embodiments, the machine learningmodel may be an artificial neural network model or a support vectorregression model. In some embodiments, the model may be trained todetermine an expected traffic control device state for one or moretraffic control devices of an intersection of the vehicle transportationnetwork.

In some embodiments, training an artificial neural network model mayinclude training a permitted, or green, right-of-way signal model, whichmay be used to determine a remaining duration, or countdown, for acurrent permitted, or green, right-of-way signal. In some embodiments,training a support vector regression model may include training apermitted, or green, right-of-way signal model, a denied right-of-waysignal model, or both. A denied right-of-way signal model may be used todetermine a remaining duration, or countdown, for a current denied, orred, right-of-way signal. In some embodiments, training a support vectorregression model may include selecting one or more kernels, such as apolynomial kernel, a laplace kernel, or both.

FIG. 9 is a diagram of an example of a method of identifying a route inaccordance with this disclosure. In some embodiments, identifying aroute may be implemented in a vehicle, such as the vehicle 1000 shown inFIG. 1 or the vehicles 2100/2110 shown in FIG. 2, which may be anautonomous vehicle.

In some embodiments, identifying a route at 7100 may include generatingone or more candidate routes at 9000, identifying an expectedintersection temporal location at 9100, identifying expected trafficstate information at 9200, determining expected traffic control devicestate information at 9300, identifying a selected route at 9400, or acombination thereof.

In some embodiments, one or more candidate routes from an origin to adestination in the vehicle transportation network may be generated at9000. For example, the candidate routes may be identified based onvehicle transportation network information, such as the vehicletransportation network information identified as shown at 7000 in FIG.7.

In some embodiments, one or more of the candidate routes may include avehicle transportation network intersection. In some embodiments, thevehicle transportation network information may include expected trafficcontrol device state information, such as expected traffic controldevice state information corresponding to the vehicle transportationnetwork intersection. For example, the expected traffic control devicestate information corresponding to the vehicle transportation networkintersection may include information representing a trained modelcorresponding to the vehicle transportation network intersection.

In some embodiments, an expected intersection temporal location of thevehicle transportation network intersection may be identified at 9100.For example, a candidate route may include an approach for the vehicletransportation network intersection controlled by a vehicletransportation network traffic control device and the expectedintersection temporal location of the vehicle transportation networkintersection may be an expected, or predicted, time, or time period, forthe vehicle to traverse the approach.

In some embodiments, expected traffic state information may beidentified at 9200. For example, expected traffic state information forthe vehicle transportation network intersection may be identified. Theexpected traffic state information may indicate an expected, orpredicted, count or cardinality of vehicles that may be in one or moreapproaches, such as the approach corresponding to a candidate route, tothe vehicle transportation network intersection in a defined time periodcorresponding to the expected intersection temporal location identifiedat 9100.

In some embodiments, expected traffic control device state informationmay be determined at 9300. For example the expected traffic controldevice state information may indicate the expected, or predicted, stateof a traffic control device, such as a traffic control device of anintersection along a candidate route. In some embodiments, the expectedtraffic control device state information may be identified based on theexpected intersection temporal location identified at 9100, the expectedtraffic state information identified at 9200, or a combination thereof.

In some embodiments, the expected traffic control device stateinformation may indicate expected permitted right-of-way signal temporalinformation corresponding to the vehicle transportation networkintersection for the route. For example, the expected traffic controldevice state information may indicate that the traffic control device isexpected to signal a permitted right-of-way signal concurrent with theexpected intersection temporal location. In some embodiments, theexpected intersection temporal location, the expected traffic stateinformation, or both, may be provided as input to the model trained forthe traffic control device, and the model may generate, or predict, theexpected traffic control device state information.

In some embodiments, a selected route may be identified at 9400. Forexample, identifying the selected route may include selecting acandidate route from the candidate routes identified at 9000. In someembodiments, a candidate route may be selected as the selected routebased on one or more operational cost metrics for traversing the vehicletransportation network from the origin, or current location of thevehicle, to the destination via the vehicle transportation networkintersection using the route, such as a passenger safety risk metric, aroute duration metric, or a fuel consumption metric. For example, basedon the expected traffic state information identified at 9200 for theintersection at the expected intersection temporal location identifiedat 9100 for traversing an approach to the intersection via the route themachine learning model trained for the intersection may indicate thatthe traffic control device for the approach will signal a denied trafficcontrol signal at the expected intersection temporal location, which mayresult in an increased route duration, and another candidate route,having a lower route duration may be selected.

FIG. 10 is a diagram of an example of a method of traversing a route inaccordance with this disclosure. In some embodiments, traversing a routemay be implemented in a vehicle, such as the vehicle 1000 shown in FIG.1 or the vehicles 2100/2110 shown in FIG. 2. In some embodiments,traversing a route at 7200 may include beginning travel at 10000,identifying expected traffic control device state information at 10100,adjusting a speed at 10200, traversing an intersection at 10300, or acombination thereof.

In some embodiments, travel at may begin at 10000. For example, avehicle may begin traversing the vehicle transportation network from anorigin to a destination via a route, such as the route selected as shownat 7100 in FIG. 7. In some embodiments, the vehicle may be an autonomousvehicle and a trajectory controller of the vehicle may operate thevehicle to traverse the route.

In some embodiments, expected traffic control device state informationmay be identified at 10100. For example, a current, or expected, routefor the vehicle may include a vehicle transportation networkintersection. In some embodiments, the vehicle transportation networkinformation may include expected traffic control device stateinformation, such as expected traffic control device state informationcorresponding to the vehicle transportation network intersection. Forexample, the expected traffic control device state informationcorresponding to the vehicle transportation network intersection mayinclude information representing a trained model corresponding to thevehicle transportation network intersection.

In some embodiments, identifying the expected traffic control devicestate information at 10100 may include identifying an expectedintersection temporal location of the vehicle transportation networkintersection, which may be similar to identifying an expectedintersection temporal location of the vehicle transportation networkintersection as shown at 9100 in FIG. 9. For example, the current routemay include an approach for the vehicle transportation networkintersection controlled by a vehicle transportation network trafficcontrol device and the expected intersection temporal location of thevehicle transportation network intersection may be an expected, orpredicted, time, or time period, for the vehicle to traverse theapproach.

In some embodiments, identifying the expected traffic control devicestate information at 10100 may include identifying expected trafficstate information, which may be similar to identifying expected trafficstat information as shown at 9200 in FIG. 9. For example, expectedtraffic state information for the vehicle transportation networkintersection may indicate an expected, or predicted, count orcardinality of vehicles that may be in one or more approaches, such asthe approach corresponding to the current route, to the vehicletransportation network intersection in a defined time periodcorresponding to the expected intersection temporal location for thevehicle to traverse the approach.

In some embodiments, the expected traffic control device stateinformation may indicate the expected, or predicted, state of a trafficcontrol device, such as a traffic control device of an intersectionalong the current route. In some embodiments, the expected trafficcontrol device state information may be identified based on the expectedintersection temporal location, the expected traffic state informationidentified, or a combination thereof.

In some embodiments, the expected intersection temporal location, theexpected traffic state information, or both, may be provided as input tothe model trained for the traffic control device, and the model maygenerate, or predict, the expected traffic control device stateinformation. For example, the expected traffic control device stateinformation may indicate that the traffic control device is expected tosignal a permitted, or a denied, right-of-way signal concurrent with theexpected intersection temporal location for the vehicle to traverse theapproach, may indicate a time corresponding to the beginning of thesignal period concurrent with the expected intersection temporallocation for the vehicle to traverse the approach, may indicate anexpected remaining duration for the signal period concurrent with theexpected intersection temporal location for the vehicle to traverse theapproach, or a combination thereof.

In some embodiments, a speed may be adjusted at 10200. For example, theexpected traffic control device state information may indicate that thetraffic control device is expected to signal a denied right-of-waysignal concurrent with the expected intersection temporal location forthe vehicle to traverse the approach, may indicate that the remainingexpected duration of the denied right-of-way signal is expected toexpire shortly after the current expected intersection temporal locationfor the vehicle to traverse the approach, the vehicle may reduce speedslightly, such as by five miles per hour, prior to traversing theapproach, and expected intersection temporal location for the vehicle totraverse the approach may be delayed to correspond with a permittedright-of-way signal, without requiring the vehicle to stop at the deniedright-of-way signal, which may minimize an expected operational costmetric, such as a route duration metric, a fuel consumption metric, or acombination thereof. In some embodiments, the vehicle may traverse theintersection at 10300.

The above-described aspects, examples, and implementations have beendescribed in order to allow easy understanding of the disclosure are notlimiting. On the contrary, the disclosure covers various modificationsand equivalent arrangements included within the scope of the appendedclaims, which scope is to be accorded the broadest interpretation so asto encompass all such modifications and equivalent structure as ispermitted under the law.

1. A vehicle comprising: a processor configured to execute instructionsstored on a non-transitory computer readable medium to: identifytransportation network information representing a vehicle transportationnetwork, the vehicle transportation network including a primarydestination, wherein identifying the transportation network informationincludes identifying the transportation network information such thatthe transportation network information includes expected traffic controldevice state information, wherein the expected traffic control devicestate information is determined using an expected traffic control devicestate determination unit, wherein the expected traffic control devicestate determination unit implements a machine learning algorithm todetermine the expected traffic control device state information, whereinthe machine learning algorithm includes an artificial neural networkalgorithm, and identify a route from an origin to the primarydestination in the vehicle transportation network using thetransportation network information, wherein the route includes a vehicletransportation network intersection, and wherein the expected trafficcontrol device state information includes expected traffic controldevice state information corresponding to the vehicle transportationnetwork intersection, and wherein using the transportation networkinformation includes using the expected traffic control device stateinformation corresponding to the vehicle transportation networkintersection; and a trajectory controller configured to operate thevehicle to traverse the vehicle transportation network from the originto the primary destination using the route.
 2. The vehicle of claim 1,wherein the vehicle is an autonomous vehicle, and wherein the trajectorycontroller is configured to operate the vehicle to traverse the vehicletransportation network from the origin to the primary destination usingthe expected traffic control device state information corresponding tothe vehicle transportation network intersection.
 3. The vehicle of claim2, wherein operating the vehicle to traverse the vehicle transportationnetwork from the origin to the primary destination using the expectedtraffic control device state information corresponding to the vehicletransportation network intersection includes adjusting a speed of thevehicle in response to the expected traffic control device stateinformation such that an expected operational cost metric for traversingthe vehicle transportation network intersection is minimized.
 4. Thevehicle of claim 3, wherein expected operational cost metric includes atleast one of a route duration metric or a fuel consumption metric. 5.The vehicle of claim 1, wherein the processor is configured to executeinstructions stored on the non-transitory computer readable medium toidentify the route by: identifying an expected temporal location of thevehicle transportation network intersection relative to operating thevehicle to traverse the vehicle transportation network from the originto the primary destination using the route; identifying expected trafficstate information for the vehicle transportation network intersectionbased on the expected temporal location; determining the expectedtraffic control device state information corresponding to the vehicletransportation network intersection based on the expected temporallocation and the expected traffic state information using the expectedtraffic control device state determination unit; and identifying theroute from a plurality of candidate routes such that an expectedoperational cost metric for traversing the vehicle transportationnetwork from the origin to the primary destination using the route isminimized based on the expected traffic control device stateinformation, and wherein expected operational cost metric includes atleast one of a passenger safety risk metric, a route duration metric, ora fuel consumption metric.
 6. The vehicle of claim 1, wherein a trafficcontrol device controller associated with the vehicle transportationnetwork intersection is a vehicle actuated traffic control devicecontroller.
 7. The vehicle of claim 1, wherein the processor isconfigured to execute instructions stored on the non-transitory computerreadable medium to identify the transportation network information suchthat the expected traffic control device state information is determinedby: receiving traffic control information for a traffic control devicecontroller associated with the vehicle transportation networkintersection; evaluating the traffic control information to identify aplurality of candidate traffic control features; determining a pluralityof selected traffic control features from the plurality of candidatetraffic control features; and training the expected traffic controldevice state determination unit corresponding to the vehicletransportation network intersection using the plurality of selectedtraffic control features.
 8. The vehicle of claim 7, wherein the trafficcontrol information for the traffic control device controller associatedwith the vehicle transportation network intersection includes trafficcontrol information for a defined temporal period.
 9. The vehicle ofclaim 8, wherein evaluating the traffic control information to identifythe plurality of candidate traffic control features includes, for eachcandidate traffic control feature from the plurality of candidatetraffic control features, determining a respective plurality of trafficcontrol feature values, such that the plurality of traffic controlfeature values includes traffic control feature values determinedaccording to a defined interval during the defined temporal period. 10.The vehicle of claim 9, wherein the defined temporal period is one weekand the defined interval is one second.
 11. The vehicle of claim 7,wherein the plurality of selected traffic control features includes atleast one of: elapsed permitted right-of-way signal temporalinformation; elapsed signal phase temporal information; elapsed cycletemporal information; a maximum lane-wise cardinality of detectedvehicles corresponding to the elapsed permitted right-of-way signaltemporal information; a plurality of permitted right-of-way signaldurations associated with the elapsed permitted right-of-way signaltemporal information, wherein each permitted right-of-way signalduration from the plurality of permitted right-of-way signal durationscorresponds with a permitted right-of-way signal from a sequence ofpermitted right-of-way signals preceding a current permittedright-of-way signal; a plurality of prior maximum permitted lane-wisecardinalities, wherein each prior maximum permitted lane-wisecardinality indicates a respective maximum permitted lane-wisecardinality of detected vehicles corresponding to a respective permittedright-of-way signal duration from the plurality of permittedright-of-way signal durations; a plurality of denied right-of-way signaldurations associated with the elapsed permitted right-of-way signaltemporal information, wherein each denied right-of-way signal durationfrom the plurality of denied right-of-way signal durations correspondswith a denied right-of-way signal from a sequence of denied right-of-waysignals preceding a current permitted right-of-way signal; a pluralityof prior maximum denied lane-wise cardinalities, wherein each priormaximum denied lane-wise cardinality indicates a respective maximumdenied lane-wise cardinality of detected vehicles corresponding to arespective denied right-of-way signal duration from the plurality ofdenied right-of-way signal durations; or a signal duration extensionfactor indicator indicating that a traffic control device right-of-waysignal duration for a traffic control device from a first signal group,corresponding to the elapsed permitted right-of-way signal temporalinformation, was extended in response to traffic control temporalinformation for a second signal group.
 12. The vehicle of claim 11,wherein the plurality of permitted right-of-way signal durationsincludes five permitted right-of-way signal durations and the pluralityof denied right-of-way signal durations includes five deniedright-of-way signal durations.
 13. The vehicle of claim 11, wherein theprocessor is configured to execute instructions stored on thenon-transitory computer readable medium to identify the transportationnetwork information such that the expected traffic control device stateinformation is determined by: generating a plurality of normalizedtraffic control feature values by normalizing one or more trafficcontrol feature values from the plurality of traffic control featurevalues.
 14. The vehicle of claim 13, wherein normalizing one or more ofthe plurality of traffic control feature values includes generating anormalized traffic control feature value from the plurality ofnormalized traffic control feature values corresponding to therespective traffic control feature value by: on a condition that therespective corresponding traffic control feature is the elapsedpermitted right-of-way signal temporal information, dividing therespective traffic control feature value by a defined maximum permittedright-of-way signal temporal value; on a condition that the respectivecorresponding traffic control feature is the elapsed signal phasetemporal information, dividing the respective traffic control featurevalue by the defined maximum permitted right-of-way signal temporalvalue; on a condition that the respective corresponding traffic controlfeature is the elapsed cycle temporal information, dividing therespective traffic control feature value by the defined maximumpermitted right-of-way signal temporal value; on a condition that therespective corresponding traffic control feature is a permittedright-of-way signal duration from the plurality of permittedright-of-way signal durations associated with the elapsed permittedright-of-way signal temporal information, dividing the respectivetraffic control feature value by the defined maximum permittedright-of-way signal temporal value; on a condition that the respectivecorresponding traffic control feature is a denied right-of-way signalduration from the plurality of denied right-of-way signal durationsassociated with the elapsed permitted right-of-way signal temporalinformation, dividing the respective traffic control feature value by adefined maximum denied right-of-way signal temporal value; on acondition that the respective corresponding traffic control feature isthe maximum lane-wise cardinality of detected vehicles corresponding tothe elapsed permitted right-of-way signal temporal information, dividingthe respective traffic control feature value by a defined lane-wisemaximum capacity; on a condition that the respective correspondingtraffic control feature is a prior maximum permitted lane-wisecardinality from the plurality of prior maximum permitted lane-wisecardinalities, dividing the respective traffic control feature value bythe defined lane-wise maximum capacity; and on a condition that therespective corresponding traffic control feature is a prior maximumdenied lane-wise cardinality from the plurality of prior maximum deniedlane-wise cardinalities, dividing the respective traffic control featurevalue by the defined lane-wise maximum capacity.
 15. The vehicle ofclaim 13, wherein the processor is configured to execute instructionsstored on the non-transitory computer readable medium to identify thetransportation network information such that the expected trafficcontrol device state information is determined by: identifying aselected traffic control feature group from the plurality of selectedtraffic control features, wherein the selected traffic control featuregroup includes selected traffic control features from the plurality ofselected traffic control features that correspond temporally with adefined temporal location within the defined temporal period.
 16. Thevehicle of claim 15, wherein training the expected traffic controldevice state determination unit includes training the expected trafficcontrol device state determination unit using the selected trafficcontrol feature group and the corresponding plurality of normalizedtraffic control feature values.
 17. The vehicle of claim 15, whereinidentifying the selected traffic control feature group includesidentifying a plurality of selected traffic control feature groups,wherein each selected traffic control feature group from the pluralityof selected traffic control feature groups includes respective selectedtraffic control features from the plurality of selected traffic controlfeatures that correspond temporally with a respective defined temporallocation from a plurality of defined temporal locations within thedefined temporal period.
 18. The vehicle of claim 17, wherein theexpected traffic control device state determination unit includes aplurality of the group-wise expected traffic control device statedetermination units, wherein each group-wise expected traffic controldevice state determination unit from the plurality of group-wiseexpected traffic control device state determination units correspondswith a respective selected traffic control feature group from theplurality of selected traffic control feature groups and whereintraining the expected traffic control device state determination unitincludes training each group-wise expected traffic control device statedetermination unit from the plurality of group-wise expected trafficcontrol device state determination units using the respectivecorresponding selected traffic control feature group and the respectivecorresponding plurality of normalized traffic control feature values.19. An autonomous vehicle comprising: a processor configured to executeinstructions stored on a non-transitory computer readable medium to:identify transportation network information representing a vehicletransportation network, the vehicle transportation network including aprimary destination, wherein identifying the transportation networkinformation includes identifying the transportation network informationsuch that the transportation network information includes expectedtraffic control device state information, wherein the expected trafficcontrol device state information is determined using an expected trafficcontrol device state determination unit, wherein the expected trafficcontrol device state determination unit implements a machine learningalgorithm to determine the expected traffic control device stateinformation, wherein the machine learning algorithm includes anartificial neural network algorithm, and identify a route from an originto the primary destination in the vehicle transportation network usingthe transportation network information, wherein the route includes avehicle transportation network intersection, and wherein the expectedtraffic control device state information includes expected trafficcontrol device state information corresponding to the vehicletransportation network intersection, and wherein using thetransportation network information includes using the expected trafficcontrol device state information corresponding to the vehicletransportation network intersection; and a trajectory controllerconfigured to operate the autonomous vehicle to traverse the vehicletransportation network from the origin to the primary destination usingthe route using the expected traffic control device state informationcorresponding to the vehicle transportation network intersection. 20.The autonomous vehicle of claim 19, wherein operating the autonomousvehicle to traverse the vehicle transportation network from the originto the primary destination using the expected traffic control devicestate information corresponding to the vehicle transportation networkintersection includes adjusting a speed of the autonomous vehicle inresponse to the expected traffic control device state information suchthat an expected operational cost metric for traversing the vehicletransportation network intersection is minimized, and wherein theexpected operational cost metric includes at least one of a routeduration metric or a fuel consumption metric.
 21. The autonomous vehicleof claim 19, wherein the processor is configured to execute instructionsstored on the non-transitory computer readable medium to identify theroute by: identifying an expected temporal location of the vehicletransportation network intersection relative to operating the autonomousvehicle to traverse the vehicle transportation network from the originto the primary destination using the route; identifying expected trafficstate information for the vehicle transportation network intersectionbased on the expected temporal location; determining the expectedtraffic control device state information corresponding to the vehicletransportation network intersection based on the expected temporallocation and the expected traffic state information using the expectedtraffic control device state determination unit; and identifying theroute from a plurality of candidate routes such that an expectedoperational cost metric for traversing the vehicle transportationnetwork from the origin to the primary destination using the route isminimized based on the expected traffic control device stateinformation.
 22. The autonomous vehicle of claim 21, wherein expectedoperational cost metric includes at least one of a passenger safety riskmetric, a route duration metric, or a fuel consumption metric.
 23. Avehicle comprising: a processor configured to execute instructionsstored on a non-transitory computer readable medium to: identifytransportation network information representing a vehicle transportationnetwork, the vehicle transportation network including a primarydestination, wherein identifying the transportation network informationincludes identifying the transportation network information such thatthe transportation network information includes expected traffic controldevice state information, wherein the expected traffic control devicestate information is determined using an artificial neural networkalgorithm, such that the expected traffic control device stateinformation is determined by: receiving traffic control information fora vehicle actuated traffic control device controller associated with thevehicle transportation network intersection; evaluating the trafficcontrol information to identify a plurality of candidate traffic controlfeatures; determining a plurality of selected traffic control featuresfrom the plurality of candidate traffic control features; and trainingthe expected traffic control device state determination unitcorresponding to the vehicle transportation network intersection usingthe plurality of selected traffic control features, and identify a routefrom an origin to the primary destination in the vehicle transportationnetwork using the transportation network information, wherein the routeincludes a vehicle transportation network intersection, and wherein theexpected traffic control device state information includes expectedtraffic control device state information corresponding to the vehicletransportation network intersection, and wherein using thetransportation network information includes using the expected trafficcontrol device state information corresponding to the vehicletransportation network intersection, wherein the expected trafficcontrol device state information includes expected permittedright-of-way signal temporal information corresponding to the vehicletransportation network intersection for the route; and a trajectorycontroller configured to operate the vehicle to traverse the vehicletransportation network from the origin to the primary destination usingthe route.
 24. The vehicle of claim 23, wherein the traffic controlinformation for the traffic control device controller associated withthe vehicle transportation network intersection includes traffic controlinformation for a defined temporal period.
 25. The vehicle of claim 24,wherein evaluating the traffic control information to identify theplurality of candidate traffic control features includes, for eachcandidate traffic control feature from the plurality of candidatetraffic control features, determining a respective plurality of trafficcontrol feature values, such that the plurality of traffic controlfeature values includes traffic control feature values determinedaccording to a defined interval during the defined temporal period, andwherein the defined temporal period is one week and the defined intervalis one second.